Cokriging based statistical approximation model for forecasting ionospheric VTEC during high solar activity and storm days

• Published in: Astrophysics and space science Vol. 364; no. 8; pp. 1 - 11
• Main Authors: Mukesh, R., Karthikeyan, V., Soma, P., Sindhu, P.
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.08.2019; Springer Nature B.V
• Subjects: Accuracy; Approximation; Astrobiology; Astronomy; Astrophysics; Astrophysics and Astroparticles; Correlation coefficient; Correlation coefficients; Cosmology; Empirical analysis; Forecasting; Geomagnetic activity; Geomagnetism; Global positioning systems; GPS; Ionosphere; Ionospheric models; Ionospheric storms; Latitude; Mathematical models; Observations and Techniques; Original Article; Orthogonal functions; Performance prediction; Physics; Physics and Astronomy; Satellite communications; Satellite navigation systems; Solar activity; Space Exploration and Astronautics; Space Sciences (including Extraterrestrial Physics; Statistical analysis; Storms; Total Electron Content; Websites; GPS; VTEC forecasting; Storm and high solar activity; Cokriging
• ISSN: 0004-640X; 1572-946X
• DOI: 10.1007/s10509-019-3612-2

The ionospheric storm is a space climate based event that takes place as a function of time, season and location. In spite of that, it is not yet possible to state how the ionosphere will react to a given space climate event since the measurements of the location, time of observation, amount of disturbances, and type of storm vary from event to event. Nevertheless, statistical analysis of observations in terms of Total Electron Content (TEC) enables one to calculate and forecast a most likely upcoming disturbed TEC, based on predictions of geomagnetic activity. The forecast of disturbed TEC is important and it helps users to know how much it affects the satellite communication systems. In this paper, GPS Vertical TEC (VTEC) data during ionospheric storm has been analyzed and storm-influenced features in the VTEC have been studied. Input parameters such as, location (Latitude & Longitude), Time, F10.7, Ap and Dst indices are collected from NASA website and its corresponding TEC values are used for construction of Cokriging (COK) based statistical approximation model. Prediction of VTEC is performed during storm and high solar activity days and for this purpose previous one-week data of different regions are used. The VTEC prediction is performed using COK model during high solar activity (2015) for IISC station. From the prediction, it is found that, our proposed COK model gives good prediction with RMSE of 6.5325 TECU, Correlation Coefficient as 0.9643 and Mean accuracy of 5.1752 TECU. Also VTEC prediction is performed over three different latitude regions during storm days (2003) and from the predicted results it is noted that, in high latitude region RMSE is 6.1848 TECU, correlation coefficient is 0.3958 and mean accuracy is 4.8105 TECU, in mid latitude RMSE is 12.7957 TECU, correlation coefficient is 0.9249 and mean accuracy is 9.2674 TECU and in low latitude RMSE is 11.5348 TECU, correlation coefficient is 0.9299 and mean accuracy is 9.2116 TECU. The results show that COK model provides good forecasting at low and mid latitude regions. Finally, prediction results of COK model is compared with the Empirical Orthogonal Function (EOF) and IRI-Plas-2017 models over Sutherland (−32.21°N, 20.81°E) station in South Africa during storm conditions. It is found that the average RMSE of EOF model is 5.91 TECU, average RMSE of the IRI-Plas-2017 model is 8.2079 TECU and average RMSE of COK model is 5.5971 TECU. Hence the prediction results show that COK model performs 5.6% better than the EOF model and 46.64% better than the IRI-Plas-2017 model. These results of the proposed COK model is encouraging and useful for indicating storm and high solar activity.